Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

Abstract Graph Convolutional Networks (GCNs), aiming to obtain the representation of a
node by aggregating its neighbors, have demonstrated great power in tackling various …

With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …

Abstract Graph Convolutional Networks (GCNs) have been widely applied in various fields
due to their significant power on processing graph-structured data. Typical GCN and its …

The identification of protein complexes in protein-protein interaction networks is the most
fundamental and essential problem for revealing the underlying mechanism of biological …